Unsupervised compressive sensing of change area in remote sensing images
遥感图像变化区域的无监督压缩感知

Traditional remote sensing image change detection approaches based on structure features are usually limited by imaging stability. In this paper, we introduce a new method for unsupervised change detection in remote sensing images using compressive sensing (CS) based on the image inherent sparse structures. For this algorithm, a large collection of image patches is projected onto high dimensional spaces through redundant dictionary, giving an adaptive sparse representation per each image patch. A random matrix is taken as measurement matrix to realize feature space dimension reduction. Then, the final change detection is realized by clustering the feature vector space using the fuzzy C-mean clustering(FCM)algorithm, achieving the reconstruction of change regional information. The experimental results demonstrate that the proposed algorithm has good change detection results both in contour and region and has a good robustness.